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Control-oriented regularization for linear system identification
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DOI：10.1016/j.automatica.2021.109539.png)
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Abstract
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In this paper, we develop a novel theoretical framework for control-oriented identification, based on a Bayesian perspective on modeling. Specifically, we show that closed-loop specifications can be incorporated within the identification procedure as a prior of the model probability distribution via suitable regularization. The corresponding kernel varies according to the additional penalty term and provides a new insight on control-oriented identification. As a secondary contribution, we derive a Bayesian robust control design approach exploiting all the information coming from the above modeling procedure, including the estimate of the uncertainty set The effectiveness of the proposed strategy against state-of-the-art regularized identification is illustrated on a benchmark example for digital control system design. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Linear system identification
Regularization
Bayesian learning
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